Maths

Install this skill (skills CLI)

Add this skill to your agent from the skills ecosystem:

# List skills in this repo
npx skills add udnisap/skills --list

# Install the maths skill (project scope)
npx skills add udnisap/skills --skill maths -y

# Install globally for all projects
npx skills add udnisap/skills --skill maths -g -y

Repo: github.com/udnisap/skills.

Setup (do this first)

Use a virtual environment and install the maths libraries inside it. Step-by-step: install.md. Install by OS (Linux, macOS, Windows): install-by-os.md.

• Check (with venv activated): python -c "import numpy, scipy, sympy, mpmath; print('OK')" — if this fails, follow install.md or install-by-os.md for your platform.
• Quick setup: create venv (e.g. python3 -m venv .venv), activate it, then pip install numpy scipy sympy mpmath. Run scripts with that venv’s python.

Use established Python libraries instead of hand-rolled math. Choose by task:

Need	Library
Arrays, linear algebra, FFT, random	NumPy
Optimization, integration, ODEs, stats, sparse	SciPy
Symbolic math, simplify, solve, differentiate	SymPy
Arbitrary-precision floats, special functions	mpmath

When to use

• Numerical arrays, matrix ops, eigenvalues, SVD, FFT
• Minimization, root finding, curve fitting, integration, ODEs
• Symbolic expressions, equation solving, calculus (symbolic)
• High-precision decimals or special functions beyond float64

Quick workflow

1. Identify numeric vs symbolic vs high-precision.
2. Import only the submodule you need (e.g. scipy.optimize, sympy.solvers).
3. Prefer library functions over custom loops (vectorize with NumPy; use scipy.integrate, sympy.integrate, etc.).

Using from the CLI (for agents)

When the agent must run maths from the command line, use Python inline:

• One-liner: python -c 'import numpy as np; print(np.linalg.det([[1,2],[3,4]]))'
• Multi-line: use a single string with semicolons, or write a short script and run python script.py. For longer code, prefer a temp file over a huge -c string.
• SymPy (expression in, result out): python -c "import sympy as sp; x=sp.Symbol('x'); print(sp.solve(x**2-4,x))"
• Exit code: script should print() the result and exit 0; the agent reads stdout. Use sys.exit(1) on error so the agent can detect failure.

Use the venv’s python (or python3); ensure the venv has the needed packages (see install.md).

Scripts

Runnable examples are in scripts/. With the venv activated, run them from the skill directory:

• scripts/numpy_solve.py — solve Ax = b (option: --eig for eigenvalues)
• scripts/scipy_minimize.py — minimize a scalar function
• scripts/scipy_integrate.py — definite integral (Gaussian)
• scripts/sympy_solve.py — solve equation (arg: expression, e.g. "x**2 - 4")
• scripts/sympy_integrate.py — symbolic integral (arg: expression, e.g. "sin(x)**2")
• scripts/mpmath_precision.py — high-precision integral (optional arg: dps)

See examples.md for exact CLI commands and one-liners.

Additional resources

• For environment setup (venv and library install), see install.md; for OS-specific install, see install-by-os.md.
• For API patterns and code snippets per library, see reference.md.
• For worked examples and CLI usage, see examples.md.